How to Embed Artificial Intelligence into Pharma Sales and Marketing Effectively
Dr. Andrée Bates
Chairman/Founder/CEO @ Eularis | AI Pharma Expert, Keynote Speaker | Neuroscientist | Our pharma clients achieve measurable exponential growth in efficiency and revenue from leveraging AI | Investor
I recently presented the plenary session at a pharma conference covering how Artificial Intelligence (AI) is transforming pharma sales and marketing, I provided examples Eularis had completed for pharma client projects.
?Several of the attendees sent me emails afterwards wanting to know more about the specific examples I gave, which were as varied as our client needs. It was interesting to learn how few of these types of applications they were familiar with, and I thought the readers of my white papers would want to know about them, too.?
I’ve written about many of these topics before, and I’m including those links at the end of each section in case you are interested in digging deeper into a specific topic.
1. Identifying Rare Disease Patients
According to Takeda Pharmaceuticals, the average time taken to diagnose a rare disease without technology is 7.6 years and comes after countless tests and physician visits. This creates a high cost to the healthcare system, not to mention much suffering for the patient. And some cases are even worse.
?Delays in diagnosis due to the rarity of the conditions can often lead to exacerbated severity. In the past, this meant many patients were never diagnosed, or the condition was so late stage by the time they were, it didn’t help them.
?Now, using AI, we can identify all of these patients within the data sets (EHR and claims data) within minutes after the initial time spent data wrangling and creating the algorithms. Then every time a new patient enters the healthcare system, that patient is immediately identified using the algorithms.
?In fact, with the potential of this approach, and the success of our work in this space, our AI project requests has moved from HCP marketing to identifying patients in both classic rare diseases, or sub populations of specific cancer types.
Eularis project case studies with AI in rare disease:
Learn more here:
2. Recommendation Engines/Suggestion Engines for Content Engagement in Marketing or Sales Calls
A lot of the big disruptive players use AI-powered clustering linked to customer data to create very personalized product (think Amazon) or content (like Netflix and Spotify) recommendations. And they aren’t the only ones. Many content marketers use this technology to improve engagement, and so do many in pharma sales and marketing.
? In sales applications we can identify the optimal next message to give an individual physician to enable greater engagement and move him or her through the customer journey faster. In marketing we use this within the same sort of approach, serving up the right content in the various channels by individual physician or patient.
?And given they are AI powered, the more data they have, the more they learn. So they get even better at making very relevant recommendations.
?Eularis project case studies with AI in recommendation engines for sales reps:
Learn more about AI for sales teams here:
https://eularis.com/why-ai-will-soon-be-indispensable-for-pharma-sales-teams/ https://eularis.com/can-artificial-intelligence-increase-sales-productivity-in-pharma/ https://eularis.com/revisiting-the-role-of-the-sale-rep-in-a-digitized-world/
?4. Precision Physician Targeting
Many pharma companies are still relying on historical information to make physician targeting decisions. But the market is not about yesterday, it is about tomorrow. Fortunately, today the available data allows us to make more precise predictions about tomorrow. By integrating AI into the physician targeting analysis, you can identify numerous things that can create strong physician targeting and results.
For example, on an appropriate time frame we should be able to predict ‘Which doctor has the most potential to write a script for a patient appropriate for our brand, today?’ and to help the rep understand ‘What should be the priority, based on the most recent data, to gain more scripts of our brand?’
?Ultimately with channel data added, we should also be able to identify, ‘What messages and channels and sales and marketing actions will enhance that outcome’. In addition, with appropriate patient data we could also identify maximum share per physician based on their patient population.
?Eularis project case studies with AI for precision physician targeting:
Take a deeper dive into this topic here:
?5. Content Marketing on Steroids with AI
Content marketing in Pharma faces steep competition as more and more companies jump into the content marketing game. AI can help you stay ahead of the pack with algorithms to produce content, source it, optimize it and distribute it so that it reaches your customers when they want it through their preferred channels.
?We now are in the era where AI tools can write content and even novels that many can’t tell apart from human work. In fact, a Japanese AI tool wrote a novella that was nominated for a prestigious literary prize.
?You need a lot of data – but there is a lot of content publically accessible on most topics, and once AI understands the rules, like most things, it beats humans at it. At the moment, many marketers are already using AI generated copy for email subject lines and ad copy, which have been shown to reduce cost per lead by 31% in one study.
?We have used this kind of AI to issue invitations to clinical trial participants and found we were able to increase acceptance by 29.3%. The more data we have, the higher this will rise. We can also apply this kind of approach for inviting participants into market research for rarer conditions to increase acceptance rates as well.
?Without AI, a lot of this is number crunching and guesswork. With AI, all is possible far faster than imaginable. So content marketers may want to start looking at revamping your content strategy with AI powered tools.
?Learn more here:
6. Customer Segmentation and Customer Personalized Marketing
While typical pharma segmentation approaches combine prescribing levels with attitude and behavior factors outside of prescribing, they tend to use a limited number of variables and are siloed by brand. You are likely to find the same physicians in multiple brand target lists within one pharma company.
?The extent to which marketers can segment their consumers comes down to the data that they have – or can get access to.??By unifying these data silos, we can find far richer customer information. And when we apply AI, we can combine unlimited customer variables into the algorithms for a 360 degree view of the customer and data in real time.
This means you can segment dynamically, honing in on preferred channels and messaging to connect with individuals based on their individual needs and behaviors at any given time.
?An individual’s behavior will change at different times for different reasons. I often use driving as an example. If I am in my home country and have to go out of the city, I will drive. However, if I am in a foreign country, I tend to use Uber. Just as location affects my transportation choice, there are variables that affect customers’ prescribing choices. And AI can account for that.
?You’ll be able to align the brand strategy with value propositions that speak to a narrow market segment. For example, what type of marketing approach does your customer appreciate? Depending on who they are and where they are in the buying cycle, they may prefer educational opportunities such as webinars or calls from sales people. AI can segment these for you and maximize your marketing budget by reaching the right people through the right channel with the right messaging.
Eularis project case studies using AI for customer segmentation:
Learn more here:
7. Next Best Action Modelling and Omni-Channel Marketing
As you may know, it can take 20-30 sales and marketing touch points before customers start prescribing or purchasing your product. The more value you can add at each touch point, the more successful you’ll be. But providing a cohesive experience across the entire customer journey can be challenging, especially in an omni-channel campaign.
?With AI-powered NBA modeling (not rules based), you can add contextually relevant and personal experiences based on the activity and needs of the individual. But it needs to be well planned to be successful. Luckily, the technology exists to predict the most likely outcome from a set of interactions with a customer or customer segment.
Combining data and advanced AI modeling allows us to identify, by customer, the next best content, in the next best channel, in the right sequence, at the right time. This results in maximum customer engagement and faster journey to the brand.
?We conducted a project doing this across numerous brands and countries and it added over a billion dollars in incremental sales. It is exciting work, yet complex, and requires the right experts be involved to achieve your goals. At the heart of the approach is a customer-focused strategy, data and data sharing, AI analytics, and technology integration. The customers’ needs must be blended with the business objectives so that it is win-win for both.
Eularis project case studies for AI powered omnichannel next best action modelling/context marketing:
Learn more here:
?8. Customer Journey Mapping
Although content management systems were a wonderful leap in technology, Artificial Intelligence takes things to a new level. You can now uncover the changing nature of the customer’s relationship with the brand, ensure that you disrupt the journey in a positive way, and fulfill all the customer’s expectations in order to maximize engagement.
?These are sorts of questions we are now answering:
???????What is the unique journey for each customer?
???????What is the optimal sequence of content for that customer to drive brand adoption?
???????What are the optimal sequences of touchpoints to drive brand adoption?
???????Which profiles of customers are best predictors of potential for increased business?
???????Which tactics drive more customer adoption in this journey?
???????What is the optimal resource allocation across digital and non-digital channels?
???????When a customer drops off the journey, which are most valuable to re-engage and what is the best way to re-engage them?
???????Which customers should we not engage reps with?
???????Which customers use a competitor brand but are vulnerable to switch with the right content and touchpoints?
???????What is the portfolio cross-sell for any specific customer (i.e. given a large portfolio of brands, we can determine the optimal sales and profit outcome)?
?Eularis project case studies using AI to map the customer journey:
You can find more on this topic here:
9. Social Listening Analysis
With so much data streaming in 24/7, social media is an obvious big data set for marketers to analyze conversations around their brand. Applying AI, brand marketers can analyze this data for all sorts of helpful insights. Here are just a few:
???????Discover who are real influencers
???????Predict future influencers
???????Know what it is about your brand(s) that’s hindering uptake
???????Perceive threats to your brand
???????Gain insight into your competitors
领英推荐
???????Identify how to improve brand perception and engagement
???????Pinpoint what caused any spikes in traffic and predict what you need to do if there is a potential problem brewing
???????Understand what content your customers are more engaged with (and by combining this with your other data, you can also strengthen the details on who should get what content to move them up the adoption curve)
???????View emerging trends
?Eularis project case studies using AI to predict market share from social listening:
You can find more on this topic here:
10. Switch Prediction of Physicians
?Knowing that AI can make accurate predictions on a individual level, we realized we could use the data we had access to for a specific client to actually predict which physicians were showing signs of switching brands. This is useful for both retention and acquisition.
?If it’s your brand they are switching away from, you need to get in front of them – with the right message and insights – to keep them loyal. If it’s a competitor brand, you need to get in front of them to demonstrate why your brand is a good match for their specific needs.
Eularis project case studies using AI to predict physician brand switch:
Learn more here:
11. Pricing and Market Access
Pre-AI, pricing in Pharma was all focused on the clinical attributes of a drug versus its competitors. This focus will not get a drug approved and reimbursed by payers anymore.
?With the shifts in this space, Eularis has been working on Artificial Intelligence powered pricing analytics that utilize real-world data on patient populations and analyze the clinical trial data alongside this. This delivers a value-based price designed to appeal to more payers than the competitors due to the increased value to them, and also weighed up to provide maximum profit to the company.
Eularis project case studies using AI to impact market access:
You can find more on this topic here:
12. Sales Forecasting Using AI
Many years ago, sales forecasting with AI was one of the more common requests we got. This is essentially a prediction-based application which uses previous sales data, competitor sales data, product comparison and relatively simple AI techniques to predict future sales.
13. AI Search Engines and Chatbots in Pharma Market Research
Google made search results more relevant to the searchers’ intent when they incorporated AI into their search algorithm Rank Brain in 2015. Through natural language processing and semantic search, relationships between similar products can be identified even when the searcher may not be fully sure of the name of item they are seeking.
?This has numerous applications we have been able apply within pharma. For example, market research knowledge. Many companies have masses of PowerPoint documents, the content of which is known by the team. But what happens when team members leave and their replacements want to find information? It is dependent on the existing team bringing the new members up to speed. That’s why knowledge can easily be lost with job transfers.
By creating a database of all your market research PowerPoint files and applying AI search to it, you effectively allow everyone on your team to have the entire database of knowledge at their fingertips. Plus, they can search it in multiple ways, even if they don’t know whether what they are seeking is in there. The AI natural language processing will be able to interpret what is required and identify the content they are seeking.
We can even layer chatbot technology on top of that, so the marketers don’t even need to type a search. They can simply ask the question like they would with Siri or Alexa.
Eularis project case studies using AI to create a chatbot that can answer natural language questions about what is in mountains of powerpoint market research documents:
You can find more on this topic here:
14. AI Enhancing Customer Call Centers
There are so many ways AI can be integrated into pharma call centers.
?A simple example would be replacing Interactive Voice Response (IVR) with AI to improve the customer experience. We are all familiar with the classic IVR in call centers. You get an automated voice asking the purpose of your call and offering options. These are simple rule-based algorithms, and thus limited to the set responses.
?By adding AI, such as natural language processing and machine learning, instead of giving a set of choices that are recognized set key words, the system can understand the question and deliver the appropriate response or action.
In addition, compared with IVR which always provides the same output based on the same input, AI could potentially provide a different output for different people, depending on what the system has learned about the person and the probability of their needs.
?We did a project a few years ago for a pharma company that wanted to use AI to capture all the data from their call center to predict topics and optimal answers for the call center teams around specific drugs. This is just the tip of the iceberg.?With all the call center data, a lot more information and insights could be gained. We could add sentiment analysis on live calls so the staff can detect early signs of annoyance before a human advisor can.
?AI could also help with is duplication of tasks. This is a particular annoyance I find when speaking with banks. After going through security prompts and speaking with someone, if you need to be transferred, you get put through security all over again.?AI combined with RPA (robotic process automation) could eliminate this need for repetition of tasks in these systems by capturing, analyzing, cross-referencing, and sharing information across platforms and channels, all without being intrusive.
AI is great at prediction when big data is involved, and let’s face it, call center data is big data. AI can identify early trends in customer behavior and provide this to the call center team so that they can handle customer needs more effectively. This could reduce drug switching and lack of adherence as well as benefit sales and marketing planning. And it could more accurately capture the customers’ voice.
?You can find more on this topic here:
15. Faster Reimbursement
Using a combination of natural language processing and machine learning to assist in faster reimbursement creates a lot of value for pharma.
?Once a drug is approved by FDA/EMA, it must be submitted to the different formularies in the US, or the different country payers if in Europe. The goals of these payers can be different depending on their specific responsibilities. For example, if a payer is responsible for total healthcare costs, they will examine both drug cost and impact of the drug (reduced hospitalizations, etc.) and showing a significant savings in hospitalization in favor of your drug would be beneficial. However, showing data on reduced hospitalizations to payer whose responsibility is solely around drug costs may not be the best use of that meeting.
?To ensure financial success of a drug, one needs to understand and incorporate the real payer drivers (for each payer influencing the decisions) in your strategy for each stage of development and commercialization.
?Using AI we can actually write the submission documents utilizing both the drivers for that formulary as well as the optimal language shown in previous submissions to work best for that formulary committee.
If we know who is on the formulary committee, we can ramp this up even more by analyzing the accessible data on the individual members of the committee to identify their drivers and utilize that knowledge in how the submission documents are drafted.
Eularis project case studies for using AI in achieving faster reimbursement:
?You can find more on this topic here:
?16. Key Opinion Leader (KOL) / Thought Leader (TL) Mapping
?Traditional and AI approaches to identifying KOLs in a given therapy area use many of the same sources, including publications, conference abstracts, Sunshine Act data and patent applications.?The main difference is that with AI, the data is constantly updated, analyzed automatically and can identify things traditional approaches miss.
The AI approach uses public data to mao KOLs and TLs in a way similar to how the CIA maps terrorists and drug cartels.?Our clients who are doing this are able to use the data in different ways to address questions across the organization from Sales & Marketing to Clinical and Discovery. These are just some of their wins:
???????Validated the strategic brand plan
???????Pressure tested the clients view regarding who were the top influencers
???????Identified blind spots in the MSL engagement strategy
???????New early phase pre-patented opportunities were uncovered
???????Rising star and fresh faces identified for recruitment
???????Better publication planning
???????Congress interaction planning and communication
???????Rapid identification of optimum influencers for clinical trials and research collaborations
Eularis project case studies using AI to identify KOLs:
You can find more on this topic here:
Eularis project case studies using AI to identify KOLs:
So, Will AI Take My Job?
I have been asked this question many times at every conference I have spoken at. There certainly is a lot AI can do, and it will allow you to achieve a lot more than you previously could. However, there is a lot AI cannot do and subject matter experts are needed.
I think of it this way. if there is anything that is almost impossible for humans to do (like identify rare disease patients from photos online – see case study here: https://eularis.com/resources/using-ai-in-precision-identification-of-patients-with-rare-diseases/ ), that is something that AI would be able to help with. Or if there is something that takes a lot of time and effort that could be automated, that is something AI can help with. By utilizing AI where you can, you are freed up to do the higher-level strategic planning aspects of your role.
Where to start
Many businesses struggle to fit AI into their sales and marketing using ad hoc projects sold by flashy AI salesmen (what I call ‘Shiny New Object’ syndrome). They are disappointed by returns after adding AI but not seeing the expected returns because they bought an AI tool that sounded like the answer to their prayers but failed to plan a solid strategy first. I see this time and time again when we are brought in to rescue failing AI projects. You must plan an AI strategy based on business objectives, then choose the appropriate AI to utilize. Never start with the tech and then figure out what you can get from it. That is back to front thinking and rarely leads to stellar results. Some teams know they have transformative amounts of data at their disposal but are unable to obtain real value because they need guidance in understanding what their data can do and planning a strategy around that first.
?There are myriad factors to take into consideration, muddying the waters and making it difficult to know where and how to begin. Eularis have often been called in to take over transformation projects for large healthcare companies after the ‘safe’ big consulting firms have failed to deliver. This happened several times in the past 6 months and in each of these cases the 'safe' consulting firms had delivered a tech first strategy.
Conclusion
Pharma commercial team leaders with the knowledge to implement the strategic, cultural changes necessary to integrate AI are able to create tremendous shareholder value. Such businesses, however, stand out as exceptions in a sea of stalled transformations and disappointing returns on investment. Investing in expert services is one reliable way to gain the understanding and skills necessary to leverage AI to its full potential. This requires a clear vision and a comprehensive roadmap, which takes into account the state of the market, the activities of investors, tech enablers and disruptors, internal resources, and, of course, the customer. Eularis works closely with pharmaceutical companies and others in the healthcare space to build these roadmaps and help businesses achieve measurable results powered by AI to solve specific commercial challenges.
For help on where to start, pharma executives can start by reaching out to the author Dr. Andrée Bates ([email protected] ) (who pioneered AI in pharma sales and marketing in 2003) for a confidential discussion on what can be achieved for their company.
And if AI in pharma interests you, subscribe to my podcast 'AI for Pharma Growth'. Links below image.
Listen on Apple
https://podcasts.apple.com/us/podcast/ai-for-pharma-growth/id1616728442
or Spotify
https://open.spotify.com/show/0RKJawP9qbvPFrhKnv3H6d
Chairman/Founder/CEO @ Eularis | AI Pharma Expert, Keynote Speaker | Neuroscientist | Our pharma clients achieve measurable exponential growth in efficiency and revenue from leveraging AI | Investor
2 年Thank you Carla Gentry - coming from a data science rock star like you, this is high praise. Much appreciated!
Data Scientist/Contractor/Influencer @ Analytical-Solution | Certified Scrum Product Owner
2 年This is what I've always loved about you... Case studies and examples instead of smoke and mirrors - you ROCK!!!
This is awesome !